Jarad Cannon, Kevin Rose, Wheeler Ruml. “Real-Time Motion Planning with Dynamic Obstacles” Proceedings of the Fifth Annual Symposium on Combinatorial Search (2012).
Description: Probably it is going to be the main guide for the project, because this article describe the exact kind of algorithm that I need to use and compare some of them, showing pros and cons of each.
Bulitko, Vadim, Yngvi Björnsson, and Ramon Lawrence. "Case-Based Subgoaling In Real-Time Heuristic Search For Video Game Pathfinding." (2014): arXiv. Web. 22 Feb. 2015.
Description: This article is going to be useful because it talk about subgoaling in the context that I am working with. I could assume each dot in the pacman game as a subgoal.
Maxim Likhachev, David Ferguson , Geoffrey Gordon, Anthony (Tony) Stentz, and Sebastian Thrun, "Anytime Dynamic A*: An Anytime, Replanning Algorithm," Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), June, 2005.
Description: The LRTA* algorithm is based in static obstacles, differently of the pacman game. So, the article describe one variation of this algorithm to deal with this situations.
I. Szita and A. Lorincz (2007) "Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man", Volume 30, pages 659-684
Description: This work describe some heuristics and the use of some algorithms in the pacman game.
Korf, R. E. 1990. Real-time heuristic search. Artificial Intelligence 42(2-3):189–211.
Description: This article is a great reference in the real time heuristic search field.
David M. Bond, Niels A. Widger, Wheeler Ruml, Xiaoxun Sun.”Real-Time Search in Dynamic Worlds” Proceedings of the Symposium on Combinatorial Search (SoCS-10), 2010.
Description: This article describe other kind of Real-Time Search in Dynamic Worlds algorithm, the
Garcıa, Adrián Ortega, and Juan Carlos Orendain Canales. "Agent Pac-Man: A Study in A* Search Method." Sistemas Inteligentes: Reportes Finales Ene-May 2014} (2014): 1. http://www.researchgate.net/profile/Gildardo_Sanchez-Ante/publication/262600223_Sistemas_Inteligentes_Reportes_Finales_Ene-May_2014/links/0f317538331511fb40000000.pdf#page=6
Description: This work describe the use of A* search method in the Pacman game and also a different behaivor to the ghosts.Q-Learning Algorithm
Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs
Deep Learning for Reinforcement Learning in Pacman
Technical Note Q-Learning
Reinforcement Learning and Function Approximation∗
Q-Learning with Linear Function Approximation
Q-learning with linear function approximation
An Analysis of Reinforcement Learning with Function Approximation
Double Q-learning
Approximate dynamic programming and reinforcement learning∗
Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot
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https://docs.google.com/document/d/1j6A34-NBcdTyjQJGvlcyn0tOgucY4wdiQMXj-rlQtzw/edit?usp=sharing
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